As of June 2022, more than *** hours of video were uploaded to YouTube every minute. This equates to approximately ****** hours of newly uploaded content per hour. The amount of content on YouTube has increased dramatically as consumer’s appetites for online video has grown. In fact, the number of video content hours uploaded every 60 seconds grew by around ** percent between 2014 and 2020. YouTube global users Online video is one of the most popular digital activities worldwide, with ** percent of internet users worldwide watching more than ** hours of online videos on a weekly basis in 2023. It was estimated that in 2023 YouTube would reach approximately *** million users worldwide. In 2022, the video platform was one of the leading media and entertainment brands worldwide, with a value of more than ** billion U.S. dollars. YouTube video content consumption The most viewed YouTube channels of all time have racked up billions of viewers, millions of subscribers and cover a wide variety of topics ranging from music to cosmetics. The YouTube channel owner with the most video views is Indian music label T-Series, which counted ****** billion lifetime views. Other popular YouTubers are gaming personalities such as PewDiePie, DanTDM and Markiplier.

Use our YouTube Videos dataset to extract detailed information from public videos and filter by video title, views, upload date, or likes. Data points include video URL, title, description, thumbnail, upload date, view count, like count, comment count, tags, and more. You can purchase the entire dataset or a customized subset, tailored to your needs. Popular use cases for this dataset include trend analysis, content performance tracking, brand monitoring, and influencer campaign optimization.

Context

YouTube is an American online video-sharing platform headquartered in San Bruno, California. The service, created in February 2005 by three former PayPal employees—Chad Hurley, Steve Chen, and Jawed Karim—was bought by Google in November 2006 for US$1.65 billion and now operates as one of the company's subsidiaries. YouTube is the second most-visited website after Google Search, according to Alexa Internet rankings.

YouTube allows users to upload, view, rate, share, add to playlists, report, comment on videos, and subscribe to other users. Available content includes video clips, TV show clips, music videos, short and documentary films, audio recordings, movie trailers, live streams, video blogging, short original videos, and educational videos.

YouTube (the world-famous video sharing website) maintains a list of the top trending videos on the platform. According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments, and likes). Note that they’re not the most-viewed videos overall for the calendar year”. Top performers on the YouTube trending list are music videos (such as the famously virile “Gangam Style”), celebrity and/or reality TV performances, and the random dude-with-a-camera viral videos that YouTube is well-known for.

This dataset is a daily record of the top trending YouTube videos.

Note that this dataset is a structurally improved version of this dataset.

Acknowledgements

This dataset was collected using the YouTube API. This Description is cited in Wikipedia.

This dataset is designed to explore multistreaming social media video as a research method used to collect semi-structured interview data. The data are provided by Dr Karen E. Sutherland and Ms Krisztina Morris from the School of Business and Creative Industries at the University of the Sunshine Coast in Queensland, Australia. The dataset is drawn from the publicly available video recording of an interview undertaken as part of the research project called: ‘Like, Share, Follow’, a multistreaming show, featuring Dr Sutherland interviewing university graduates about their career journeys, that is broadcast across Facebook, LinkedIn, and Twitter and later uploaded to YouTube. This dataset examines how multistreaming video interview data can be used to answer research questions and the benefits and challenges this specific method of data collection can pose in the process of data analysis. The video example is accompanied by a teaching guide and a student guide.

We used the YouTube Data API to augment the YouTube 8M corpus by crawling a variety of meta data for the videos.

First point of interest was the "video resource," which comprises data about the video, such as the video’s title, description, uploader name, tags, view count, and more. Also included in the meta data is whether comments have been left for the video. If so, we downloaded them as well, including information about their authors, likes, dislikes, and responses.

There is no property which specifies a video’s language, since this information is not mandatory when uploading a video. Also, the API provides only information about the available captions, but not the captions themselves. Only the uploader of a video is given access to its captions via the API; we extracted them using youtube-dl. For each video, all manually created captions were downloaded, and auto-generated captions in the "default" language and English. The "default" auto-generated caption gives perhaps the only hint at a video’s original language.

Finally, we downloaded all thumbnails used to advertise a video, which are not available via the API, but only via a canonical URL. Our corpus provides the possibility to recreate the way a video is presented on YouTube (meta data and thumbnail), what the actual content is ((sub)titles and descriptions), and how its viewers reacted (comments).

If you use this dataset in your publication, please cite the dataset as outlined in the right column.

As of February 2025, India was the country with the largest YouTube audience by far, with approximately 491 million users engaging with the popular social video platform. The United States followed, with around 253 million YouTube viewers. Brazil came in third, with 144 million users watching content on YouTube. The United Kingdom saw around 54.8 million internet users engaging with the platform in the examined period. What country has the highest percentage of YouTube users? In July 2024, the United Arab Emirates was the country with the highest YouTube penetration worldwide, as around 94 percent of the country's digital population engaged with the service. In 2024, YouTube counted around 100 million paid subscribers for its YouTube Music and YouTube Premium services. YouTube mobile markets In 2024, YouTube was among the most popular social media platforms worldwide. In terms of revenues, the YouTube app generated approximately 28 million U.S. dollars in revenues in the United States in January 2024, as well as 19 million U.S. dollars in Japan.

YouTube is an American online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day. As of May 2019, videos were being uploaded at a rate of more than 500 hours of content per minute.

Youtube is very much used to influence, educate, free university (for me also) people (the users followers) in a particular way for a specific issue - which can impact the order in some ways.

This dataset and supplementary tables are released in conjunction with the TPDL 2025 paper titled “How Retracted Research Persists on YouTube: Retraction Severity, Visibility, and Disclosure.” They provide detailed information used in the analysis to promote transparency, ensure reproducibility, and facilitate future studies on scholarly communication and retractions.

The dataset contains the following files:

Filename	Data Format	Description
01_dataset_scholarly_references_on_YouTube.json.gz	JSON Lines	An integrated dataset of scholarly references in YouTube video descriptions, covering videos posted up to the end of December 2023. This dataset combines the Altmetric dataset and the YA Domain Dataset and is the basis for identifying references to retracted articles. This dataset contains 743,529 scholarly references (386,628 unique DOIs) found in 322,521 YouTube videos uploaded by 77,974 channels.
02_dataset_references_to_retracted_articles_on_YouTube.json.gz	JSON Lines	A dataset of retracted articles referenced in YouTube videos, used as the primary source for analysis in this paper. The dataset was created by cross-referencing the integrated reference dataset with the Retraction Watch database. It includes metadata such as DOI, article title, retraction reason, and severity classification (Severe, Moderate, or Minor) based on Woo and Walsh (2024), along with video- and channel-level statistics (e.g., view counts and subscriber counts) retrieved via the YouTube Data API v3 as of April 22, 2025. This dataset contains 1,002 retracted articles (360 unique DOIs) found in 956 YouTube videos uploaded by 714 channels.
03_full_list_table3_sorted_by_reference_count_retracted_articles_on_YouTube.json.gz	JSON Lines	Complete list corresponding to Table 3, "Top 7 retracted articles ranked by the number of YouTube videos in which they are referenced." in the paper.
04_full_list_table5_top10_most-viewed_video.json.gz	JSON Lines	Complete list corresponding to Table 5, "Top 10 most-viewed YouTube videos that reference retracted articles, sorted by video view count." in the paper.
05_detailed_manual_coding_40_sampled_retracted_articles.xlsx	XLSX	This file provides detailed annotations for a manually coded sample of 40 YouTube videos referencing retracted scholarly articles. The sample includes 10 randomly selected videos from each of the four analytical groups categorized by publication timing (before/after retraction) and retraction severity (Moderate/Severe). The file includes reference stance for each video, visual/verbal mention of the article, and relevant timestamps when applicable. This dataset supplements the manual analysis results presented in Tables 6 and 7 in paper.

Due to concerns over potential misuse (e.g., identification or harassment of individual content creators), this dataset is not made publicly available.
Researchers who wish to use this dataset for scholarly purposes may contact the authors to request access.

References

• Woo, S., Walsh, J.P.: On the shoulders of fallen giants: What do references to retracted research tell us about citation behaviors? Quantitative Science Studies 5(1), 1–30 (2024). https://doi.org/10.1162/qss_a_00303
• Kikkawa, J., Takaku, M.: How Retracted Article Persists on YouTube: Retraction Severity, Visibility, and Disclosure. Accepted for publication in the Proceedings of the 29th International Conference on Theory and Practice of Digital Libraries (TPDL 2025).
• Accepted Papers (TPDL2025) - https://tpdl2025.github.io/Program/accepted_papers.html

Fundings

JSPS KAKENHI Grant Numbers JP22K18147 and JP23K11761.

The dataset is first introduced in the following paper: Siqi Wu, Marian-Andrei Rizoiu, and Lexing Xie. Beyond Views: Measuring and Predicting Engagement in Online Videos. In AAAI International Conference on Weblogs and Social Media (ICWSM), 2018. Tweeted videos dataset This dataset contains YouTube videos published between July 1st and August 31st, 2016. To be collected, the video needs (a) be mentioned on Twitter during aforementioned collection period; (b) have insight statistics available; (c) have at least 100 views within the first 30 days after upload. Quality videos datasets These datasets contain videos deemed of high quality by domain experts. Vevo videos: Videos of verified Vevo artists, as of August 31st, 2016. Billboard16 videos: Videos of 2016 Billboard Hot 100 chart. Top news videos: Videos of top 100 most viewed News channels. freebase_mid_type_name.csv It maps a freebase mid to a real-world entity. See more details in this data description.

YouTube Crisis Actor Videos and Recommendations

Dataset of Crisis Actor Videos on YouTube and their Recommendations

By Jonathan A. [source]

About this dataset

This dataset provides valuable insights into crisis actor videos and their corresponding recommendations on YouTube. It consists of a total of 8823 videos, accounting for an astounding 3,956,454,363 views. These videos were retrieved from YouTube's API and cover various categories and topics.

Specifically, this dataset focuses on crisis actor videos related to mass shootings, false flags, and other conspiracy theories that comprise around 20% of the collection. The remaining 80% explores conspiracies revolving around history, government institutions, and religions.

The dataset includes essential information such as the name and channel of the video uploader. Additionally, it provides details about viewer engagement through likes and dislikes counts. Furthermore, each video is assigned a category or topic to facilitate analysis.

It is important to note that approximately 100 music videos were excluded from the initial data set to maintain relevance to crisis actors.

Overall, this project aims to shed light on the prevalent issue of crisis actors on YouTube by providing researchers with a comprehensive dataset for further exploration and analysis. This highly informative dataset serves as a valuable resource for investigating trends within crisis actor content while contributing towards raising public awareness surrounding this topic

How to use the dataset

• Understanding the Dataset:

The dataset comprises several columns that provide specific information about each video and its corresponding recommendations. Here's a brief overview of the key columns:

• name: The title or name of the YouTube video.
• channel: The name of the YouTube channel that uploaded the video.
• category: The category or topic of the video.
• views: The number of views the video has received.
• likes: The number of likes received by each video.

• dislikes: The number of dislikes received by each video.

• Exploring Categories:

One way to analyze this dataset is by examining different categories mentioned in each video entry. This could involve identifying patterns within categories or comparing engagement metrics (views, likes, dislikes) across various topics.

For example, you might want to investigate how crisis actor videos are categorized compared to other conspiracy-related videos present in this dataset.

• Analyzing Engagement Metrics:

To gain insights into users' response towards different videos related to crisis actors or conspiracy theories, it is recommended that you examine engagement metrics such as views, likes, and dislikes.

You can compare these metrics between individual videos within specific categories or observe trends across all entries.

• Investigating Popularity:

Understanding which channels have maximum viewership within this particular subject area can offer valuable information for further analysis.

Examining which channels have consistently high views or engagement metrics (likes/dislikes) can help identify influential content creators related to crisis actors or conspiracy theories.

• Identifying Recommendations:

The dataset also provides information about the recommendations associated with each video entry. By analyzing these recommendations, you can gain insights into the video content YouTube suggests to users who view crisis actor videos.

You could focus on specific keywords within recommendation titles or explore patterns in terms of topic relevance or common recommendations across multiple entries.

• Cross-Referencing External Information:

As this dataset does not provide detailed descriptions or context for each video, it is advisable to cross-reference external sources to gather additional information if needed.

By using the provided video titles and channel names, you can search for more details about specific videos

Research Ideas

• Analyzing the correlation between likes, dislikes, and views: This dataset can be used to analyze the relationship between the number of likes and dislikes a video receives and its overall views. By examining this relationship, one could gain insights into factors that contribute to increased engagement or disinterest in crisis actor videos.
• Identifying popular YouTube channels in the crisis actor category: By analyzing the dataset, one can identify which YouTube channels have uploaded the most crisis actor videos and have gained high viewership. Th...

Gum bleeding is a common dental problem, and numerous patients seek health-related information on this topic online. The YouTube website is a popular resource for people searching for medical information. To our knowledge, no recent study has evaluated content related to bleeding gums on YouTube™. Therefore, this study aimed to conduct a quantitative and qualitative analysis of YouTube videos related to bleeding gums. A search was performed on YouTube using the keyword "bleeding gums" from Google Trends. Of the first 200 results, 107 videos met the inclusion criteria. The descriptive statistics for the videos included the time since upload, the video length, and the number of likes, views, comments, subscribers, and viewing rates. The global quality score (GQS), usefulness score, and DISCERN were used to evaluate the video quality. Statistical analysis was performed using the Kruskal–Wallis test, Mann–Whitney test, and Spearman correlation analysis. The majority (n = 69, 64.48%) of the videos observed were uploaded by hospitals/clinics and dentists/specialists. The highest coverage was for symptoms (95.33%). Only 14.02% of the videos were classified as "good". The average video length of the videos rated as "good" was significantly longer than the other groups (p <0.05), and the average viewing rate of the videos rated as "poor" (63,943.68%) was substantially higher than the other groups (p <0.05). YouTube videos on bleeding gums were of moderate quality, but their content was incomplete and unreliable. Incorrect and inadequate content can significantly influence patients’ attitudes and medical decisions. Effort needs to be expended by dental professionals, organizations, and the YouTube platform to ensure that YouTube can serve as a reliable source of information on bleeding gums.

Creative Commons YouTube

  Description

YouTube is large-scale video-sharing platform where users have the option of uploading content under a CC BY license. To collect high-quality speech-based textual content and combat the rampant license laundering on YouTube, we manually curated a set of over 2,000 YouTube channels that consistently release original openly licensed content containing speech. The resulting collection spans a wide range of genres, including lectures… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/youtube.

Emergency evacuations. 126 publicly available videos in which people are or should be evacuating. Sources: YouTube & news sites. See detailed information about the videos and data collection method in the excel file: vanderWal2020-details-onlinerepository.xlsxA few videos could not be uploaded to figshare, please see excel file for the source to download yourself or request complete set in a zip file (also could not upload the zip file to figshare).

Abstract

This study aimed to assess the quality and reliability of the most-watched YouTube videos on Otago exercises. The keywords “Otago exercise” and “Otago exercise program” were searched between December 15-30, 2023. Sixty videos were selected for each keyword, sorted by number of views. Video metrics and upload sources were documented. The modified (m) DISCERN score and the Global Quality Score (GQS) were used to evaluate the reliability and quality of the videos, respectively. Out of the 34 videos reviewed, the majority (47.1%) were shared by physiotherapists. The median mDISCERN score was 2, indicating that a significant proportion (79.4%) of the videos exhibited low reliability (p<0.05). The median GQS score was 3, with 64.7% of videos classified as intermediate or high quality. However, no statistical differences in quality were observed (p>0.05). Although no statistical difference was noted, it was evident that physiotherapists uploaded a higher percentage of reliable and high-quality videos compared to other sources. Analysis of the video metrics among the quality groups revealed significant differences only in video duration (p<0.05). Positive correlations were found between certain video metrics and mDISCERN (video duration, number of comments) and GQS (video duration) scores. YouTube videos on OTAGO exercises demonstrate insufficient reliability and quality. Collaboration with professional organizations in geriatric rehabilitation is recommended for YouTube to produce high-quality and reliable videos aligned with their evolving health content policy.

Anansi Masters - the story continues

The Anansi Masters project is developed by Vista Far Reaching Visuals (Mr. Jean Hellwig) and partners. It is designed as a public digital platform at http://www.anansimasters.net and opened in 2007. At the website one can find information about the story character of Nanzi (or Anansi or Kweku Ananse), with English and Dutch subtitled video recordings of storytelling in several countries in different languages, educational modules about storytelling for use at schools and academies, and digital issues of the Anansi Masters Journal published since the beginning of the project. All storytelling videos and videos that were made for documentation or marketing purposes are published on Youtube. Since 2012 all films of Anansi Masters were uploaded to Youtube and linked to the Anansi Masters website. Their display is embedded in the website together with the respective metadata that are entered through a custom made content management system (CMS).

In March 2012, public storytelling events were organized by Drs. Jean Hellwig (Hellwig Productions AV / Vista Far Reaching Visuals Foundation) on the islands of Curacao and Aruba. Any professional or non-professional storyteller was invited to tell a story in front of the Anansi Masters camera and the available audience. Storytellers were free to choose their story and language. Each storyteller had to agree that the video registration of their story could be made available for open access. Storytellers were asked in front of the camera to answer a few questions about who they are and how they selected the story that they told. The Anansi Masters project started in 2007 with the registration of Kweku Ananse stories in Ghana and The Netherlands. The storytelling events organized on Curacao and Aruba in 2012 were part of the second phase 'Anansi Masters - the story continues'. The project registers contemporary ways of storytelling from an old tradition and aims to stimulate and revitalize the Nanzi storytelling by making the storytelling videos available to a large international audience. In 2008 a dvd in Dutch was released with 22 stories from Ghana and The Netherlands. In 2013 a dvd in English is released with all 32 stories that were recorded on Curaçao and Aruba.

The stories of the Anansi tradition originate in Africa and were exported to other parts of the world through slave trade and migration. In Anansi Masters, the similarities and differences between the stories and storytellers, who tell in their own language, can be found. Anansi Masters initiates different activities all over the world where stories from this oral tradition can be found. The founder has the ambition to film as many stories from this tradition as possible in as many countries as possible. Anansi Masters collaborates with writers, theatre makers, filmmakers, researchers, schools and of course with many many storytellers.

This dataset contains the documentation, video files, documents and pictures that were made to document the second phase of the Anansi Masters project with the subtitle 'the story continues'. These files were produced to report the process and results to the sponsoring funds and to be used in marketing through Facebook.

This dataset contains the following: - report in Dutch with separate appendices - videos with datasheets 0015 - 0022 reflecting some of performances in the media to market the storytelling events - short video impression with datasheet 0023 of a musical performance at the storytelling event in Curacao - a list with names and codes of the recorded stories and storytellers

For each storyteller and their stories a new dataset has been created. Links to these datasets can be found under 'Relations'.

FruQ-DB is a dataset containing 11 varieties of fruits images. These images were created from YouTube fruit time-lapse videos (see link to the videos below). The FruQ-DB database consist of image frames from fruits such as banana, cucumber, grape, kaki, papaya, peach, pear, pepper, strawberry, tomatoes, and watermelon. A total number of 5647 preprocessed images with de-watermarked and resized (224x224). Three classes of fruit quality with number of image samples per class is summarized below:

1. Fresh (2182 images)
2. Mild (1364 images)
3. Rotten (2101 images)

Another datasets is uploaded FruQ-Multi is another folder containing each types of fruits and their classes.

Links to the YouTube videos for the different fruit time elapse are:

Avocado https://www.youtube.com/watch?v=FeQehUXZYPk

Banana https://www.youtube.com/watch?v=OmcXo9XC6Uc

Cucumber https://www.youtube.com/watch?v=UMnevucxOug

Kaki (persimmon) https://www.youtube.com/watch?v=xE0Pw7jeOBo

Papaya https://www.youtube.com/watch?v=M8scWymSp2Y

Peach https://www.youtube.com/watch?v=g9pf19wk0-E&t=367s

Pepper https://www.youtube.com/watch?v=H0Sd6Foaepk

Strawberry https://www.youtube.com/watch?v=UMnevucxOug

Tomato https://www.youtube.com/watch?v=6xEcoU1vAZk

Watermelon https://www.youtube.com/watch?v=S12zZhdOckc

Content

VGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube. There are 310+ classes, 200000+ videos, 550+ hours. VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications. All videos are captured "in the wild" with audio-visual correspondence in the sense that the sound source is visually evident. Consists of both audio and video. Each segment is 10 seconds long.

Provided a csv file. For each YouTube video, there are YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by:

YouTube ID, start seconds, label, train/test split.

Dataset with the most relevant data when evaluating the popularity/success of audiovisual content uploaded to the world-renowned Youtube platform.

Information without pre-preprocessing and/or transforming.

Info extracted through WebScraping techniques, obtaining a sample of the top 50 videos of "Youtube Trends".

Link: https://www.youtube.com/feed/trending?bp=6gQJRkVleHBsb3Jl

DataScience UOC Project

VGGSound

VGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube.

Homepage: https://www.robots.ox.ac.uk/~vgg/data/vggsound/ Paper: https://arxiv.org/abs/2004.14368 Github: https://github.com/hche11/VGGSound

  Analysis

310+ classes: VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications. 200,000+ videos: All… See the full description on the dataset page: https://huggingface.co/datasets/Loie/VGGSound.